{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>anger</th>\n",
       "      <th>anticipation</th>\n",
       "      <th>disgust</th>\n",
       "      <th>fear</th>\n",
       "      <th>joy</th>\n",
       "      <th>love</th>\n",
       "      <th>optimism</th>\n",
       "      <th>pessimism</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surprise</th>\n",
       "      <th>trust</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>He was answering a question about the criticis...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>I'm going to start today's discussion thread w...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>By announcing the 395 self-quarantined, it pai...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Likewise, sorry if I offended you. I’m not act...</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>People infected by experience high fever, coug...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                               text  anger  anticipation  \\\n",
       "0   0  He was answering a question about the criticis...      1             0   \n",
       "1   1  I'm going to start today's discussion thread w...      1             1   \n",
       "2   2  By announcing the 395 self-quarantined, it pai...      1             1   \n",
       "3   3  Likewise, sorry if I offended you. I’m not act...      1             0   \n",
       "4   4  People infected by experience high fever, coug...      0             0   \n",
       "\n",
       "   disgust  fear  joy  love  optimism  pessimism  sadness  surprise  trust  \\\n",
       "0        1     0    0     0         0          1        0         0      0   \n",
       "1        1     1    0     0         0          1        0         0      0   \n",
       "2        1     1    0     0         0          1        0         0      0   \n",
       "3        1     1    0     0         0          1        0         0      0   \n",
       "4        0     0    0     0         0          0        0         0      0   \n",
       "\n",
       "   neutral  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read dataset\n",
    "# we will be using trained dataset to understand how people are reacting\n",
    "data = pd.read_csv(\"nlp_train.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>anger</th>\n",
       "      <th>anticipation</th>\n",
       "      <th>disgust</th>\n",
       "      <th>fear</th>\n",
       "      <th>joy</th>\n",
       "      <th>love</th>\n",
       "      <th>optimism</th>\n",
       "      <th>pessimism</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surprise</th>\n",
       "      <th>trust</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>746.000000</td>\n",
       "      <td>0.364367</td>\n",
       "      <td>0.503014</td>\n",
       "      <td>0.454119</td>\n",
       "      <td>0.454119</td>\n",
       "      <td>0.123912</td>\n",
       "      <td>0.092431</td>\n",
       "      <td>0.328198</td>\n",
       "      <td>0.432686</td>\n",
       "      <td>0.277294</td>\n",
       "      <td>0.108506</td>\n",
       "      <td>0.168118</td>\n",
       "      <td>0.113195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>431.136289</td>\n",
       "      <td>0.481413</td>\n",
       "      <td>0.500158</td>\n",
       "      <td>0.498057</td>\n",
       "      <td>0.498057</td>\n",
       "      <td>0.329591</td>\n",
       "      <td>0.289731</td>\n",
       "      <td>0.469715</td>\n",
       "      <td>0.495614</td>\n",
       "      <td>0.447813</td>\n",
       "      <td>0.311123</td>\n",
       "      <td>0.374096</td>\n",
       "      <td>0.316937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>746.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1119.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1492.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id        anger  anticipation      disgust         fear  \\\n",
       "count  1493.000000  1493.000000   1493.000000  1493.000000  1493.000000   \n",
       "mean    746.000000     0.364367      0.503014     0.454119     0.454119   \n",
       "std     431.136289     0.481413      0.500158     0.498057     0.498057   \n",
       "min       0.000000     0.000000      0.000000     0.000000     0.000000   \n",
       "25%     373.000000     0.000000      0.000000     0.000000     0.000000   \n",
       "50%     746.000000     0.000000      1.000000     0.000000     0.000000   \n",
       "75%    1119.000000     1.000000      1.000000     1.000000     1.000000   \n",
       "max    1492.000000     1.000000      1.000000     1.000000     1.000000   \n",
       "\n",
       "               joy         love     optimism    pessimism      sadness  \\\n",
       "count  1493.000000  1493.000000  1493.000000  1493.000000  1493.000000   \n",
       "mean      0.123912     0.092431     0.328198     0.432686     0.277294   \n",
       "std       0.329591     0.289731     0.469715     0.495614     0.447813   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     1.000000     1.000000     1.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "          surprise        trust      neutral  \n",
       "count  1493.000000  1493.000000  1493.000000  \n",
       "mean      0.108506     0.168118     0.113195  \n",
       "std       0.311123     0.374096     0.316937  \n",
       "min       0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#basic stats\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    949\n",
       "1    544\n",
       "Name: anger, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['anger'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "freqs = {\"anger\":data['anger'].value_counts()[1],\n",
    "\"anticipation\": data['anticipation'].value_counts()[1],\n",
    "\"disgust\":data['disgust'].value_counts()[1],\n",
    "\"fear\":data['fear'].value_counts()[1],\n",
    "\"joy\":data['joy'].value_counts()[1],\n",
    "\"love\":data['love'].value_counts()[1],\n",
    "\"optimism\":data['optimism'].value_counts()[1],\n",
    "\"pessimism\":data['pessimism'].value_counts()[1],\n",
    "\"sadness\":data['sadness'].value_counts()[1],\n",
    "\"surprise\":data['surprise'].value_counts()[1],\n",
    "\"trust\":data['trust'].value_counts()[1],\n",
    "\"neutral\":data['neutral'].value_counts()[1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'anger': 544,\n",
       " 'anticipation': 751,\n",
       " 'disgust': 678,\n",
       " 'fear': 678,\n",
       " 'joy': 185,\n",
       " 'love': 138,\n",
       " 'optimism': 490,\n",
       " 'pessimism': 646,\n",
       " 'sadness': 414,\n",
       " 'surprise': 162,\n",
       " 'trust': 251,\n",
       " 'neutral': 169}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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niLie7LPtSnatxaFkPeP8ACCl9HpEHEB2E7hHI2Ii2XfZiayXmkOA14D9GmvKRJZQ/JpsvXiwmc3BDomI2jtHr0F25+j9yJKGk1JKNzR1QhFxNFB7/4w+QNeIOD1//2pK6ZqCumeTNW+ZTHYNx9Zk12n8m4Z3Om6Oa8i6Bf5hRPw6pfTflNITEVFN1h7/qYi4AXid7HvbhqyNfoOkr8DfyH6L4/L1bTbZd3s0WbOlzYvqP0b2u/2/iPg7WeLx55TSvPyz+jPwdERcSdZEakWy9fcgsoMEE/Lp/JCsudoj+fagtjvW1trHGEvW7OqSPAl/mOyC7XXJth213UMTEfuTnU0cX7Cu/ADYGbgsIp5I2X1BIPs8hgPjI+J+sm3V31LDLpOl5Vd7d+vkw4ePpXtQvjvWBGyU1x1AI90j0nh3o8Mp0TUiWZ/ut5BdhLyQrP/3k4FORfVWIOtxaDbZjkldV46U6I41L1+fbMdqHtnO7MtkR2hXLKpXcvx82EwKuqok6xr1PrKdio/Jeri5C9itiZ9zY59PyfKC4SuR9ar0cj7fefmyrdfI9zi83LI0ZfmXEM9XyI7Wz87jeY+sW9ezgf4l6q+Zf3/Tya7leJ/sTEI1Bd3GNjKvXmT3y0jA0UuoN5PGu2OtfSzMP7u/kt23YoNm/FYepPHfyINFdQ8i6z72bbJmXi+SXei9WgXzq/1+Bjcy/Lh8eHVR+T5k9/+o/W3NIruQ/3vl1guyI+t3k12D8F6+zLtQolvkfN28giyJrf1tDikYvh7Z0fmZ+bryVr6unAesUzSt/0d2JvKj/Hv6DbAZrdAda16nK9mF608WrJcvkh1Y2C2vs3Ye8zNAj6LxB+bj/ZV8m0XWe9yvyZK12s/jkErXMx8+luVHpGSTPkmSJElL5jUOkiRJksoycZAkSZJUlomDJEmSpLJMHCRJkiSVZeIgSZIkqaxl5j4OvXv3TgMGDGjvMCRJkqTPtalTp76ZUupTXL7MJA4DBgxgypQp7R2GJEmS9LkWEa+WKrepkiRJkqSyTBwkSZIklWXiIEmSJKksEwdJkiRJZZk4SJIkSSrLxEGSJElSWSYOkiRJksoycZAkSZJUlomDJEmSpLJMHCRJkiSVZeIgSZIkqazO7R2APp/GTntzqacxeqveLRCJJEmS2oJnHCRJkiSVZeIgSZIkqSwTB0mSJEllmThIkiRJKsvEQZIkSVJZJg6SJEmSyjJxkCRJklSWiYMkSZKkskwcJEmSJJVl4iBJkpSLiLKPBx98sL3DrPPhhx8yZswYnnnmmSaPM2XKFA455BD69etH165dWXvttfn2t7/NCy+8UFdnzTXX5PTTT2+NkCuy/fbb133uXbt2pV+/fnzlK1/h0ksv5eOPP65X97e//S0RwaJFi5o07eeee44xY8bw/vvvN6n+888/T0Rw//3315W15Od08cUXc8cddzQo7yjfBUDn9g5AkiSpo3j00UfrXi9YsIDdd9+d008/nX322aeufNNNN22P0Er68MMPqampYZNNNmGzzTYrW/+6667jm9/8JkOHDuVXv/oV/fv3Z9asWfz+979nyJAhzJkzpw2irsxee+3FmDFj+PTTT5k/fz5//vOf+cEPfsCVV17J/fffz8orrwzAQQcdxKBBg+jcuWm7t8899xw1NTV897vfpWfPnmXrDxgwgEcffbTVvv+LL76YnXfemX333bde+V133UXfvn1bZZ6VMnGQJEnKbb/99nWva49Eb7jhhvXKl8aCBQvo0aNHi0yrUq+++iojRozg2GOPZfz48fWGHXnkkSWPdncEvXv3rvf5H3DAARxzzDHsvPPOnHzyyVxyySUA9O3bt9V2sD/66CO6d+/eYutBJbbeeus2n2djbKokSZJUoVmzZnHMMcew/vrr06NHDzbeeGNqamr45JNP6urUNm2ZNGkSRxxxBKuuuiqHHnookCUQ3/nOd1hllVXo3bs3p512Gueffz7du3evN5/58+czYsQI+vbtS48ePdhll12YOnUqkO3M9unTB4BvfOMbdU163njjjZIxX3rppQBccMEFJYcXH+ku9PDDD7PvvvvSv39/evbsydZbb80NN9xQr85bb73F8OHD6d+/P927d2e99dZj1KhRdcNnzpzJQQcdRJ8+fejRowdVVVWcffbZjc5zSbbZZhuOO+44JkyYwIIFC4CGTZVSSpx11llssMEGdO/enTXXXJOvfe1rvPXWW9x9991130X//v2JCDbZZJN603nyySfZZZdd6NGjB7/+9a9LNlWqnc+ZZ55Jv379WHnllTnmmGPqNX9qrAlVYROk7bffnmeffZZLL7207nu8/vrrG9SrNXHiRAYOHEi3bt1Yd911687IFM9z+vTp7L777qy44opsuummS50ctskZh4jYGPhDQdEGwJnA1Xn5AGAmcFhK6Z18nFOBEcCnwIkppXvaIlZJkqRy5s2bx5prrskvf/lLVlttNaZPn86YMWN4++23ueiii+rVPemkkzjssMP44x//WNeM5qSTTuLaa6/lvPPOo6qqissuu4wnnnii3ngLFixgt912Y+HChVx44YWsscYa/PrXv2bo0KHMmDGDNdZYg7vvvpu9996bs88+m6985SsArLHGGiVjfuihh9hhhx1YddVVK17eV199lSFDhjBq1Ci6du3Kww8/zDe+8Q06d+7MgQceCMAJJ5zA008/za9+9Sv69u3La6+9Vq/p1xFHHEHnzp25/PLLWWWVVfjXv/7Fyy+/XHEstfbYYw8uuugi/vGPf5Q8E3DZZZcxbtw4fvazn/GlL32J+fPnc//997NgwQJ22GEHzj33XE477TTuvPNOVl999QZngg4//HBGjRrFWWedxeqrr95oHBMmTOBLX/oSV155Ja+99hqnnHIKixcv5pprrmnyslxxxRXst99+bLnllpx88skAVFVVlax7++23c9RRRzFixAguvPBCpk6dSnV1Nf/5z3/45S9/Wa/usGHDOO644xg9ejTjxo3j0EMPZebMmfTr16/JsRVqk8QhpfQCMAggIjoB/wZuBkYDD6SUxkbE6Pz9KRGxKTAMGAh8Abg/Ir6YUvq05AwkSZLa0DbbbMM222wDZEecd9ppJ7p27cr3v/99LrzwQjp16lRXd9ddd623Q/fGG2/wu9/9jnHjxnHCCScAWTv+jTfeuN48rrzySv71r38xffp0BgwYAMDuu+/ORhttxEUXXcTZZ59dF8NGG21UthnNv//9b3bbbbdmLe/RRx9d93rx4sXsuuuuzJw5k8suu6wucZg8eTKnnHJK3ZH8wvFSSjzxxBPcdddd7LHHHgDNjqXW2muvDcDcuXNLDp88eTL77rsvxx13XF3ZwQcfXPe6dsd86623Zs0112ww/o9+9KN64z7//PMl57No0SLuuOOOurNFXbt2ZeTIkYwZM4YNN9ywScsycOBAevToQd++fct+j2eccQZ77703l19+OZCtO4sWLeLss8/mtNNOq9dc65RTTuGII44AYPPNN+cLX/gCf/rTnxg+fHiT4irWHk2VhgL/Sim9CuwPXJWXXwUckL/eH7g+pbQwpfQKMAPYts0jlSRJKmHx4sVccMEFbLLJJvTo0YMuXbowYsQI3n///QYXGBdeWA3w1FNP8cknn7DffvvVla2wwgoN6t1///1st912rL322ixatIhFixbRqVMndtllF6ZMmdKsuCOiWeO99dZbjBo1inXXXZeuXbvSpUsXrr76al588cW6OoMGDeK8887jt7/9LTNmzGgw3y233JIf//jHXH311cyePbtZcRRKKS1x+KBBg7jllls466yzmDJlCosXL65o+sXfR2P23nvvek3MDjroIBYvXlzXpKwlLVy4kKeffrpecgbZ2ZFFixbx+OOP1yvfc889617379+fXr16LdVn3x6JwzDguvx1v5TSHID8uTZFWguYVTDO7LxMkiSp3Z1//vmcdtppHH744dx+++1MnjyZX/ziF0B27UGh4mYhtdcg1F6fUKv4/ZtvvslDDz1Ely5d6j2uu+46Zs2aRaXWWmstXnvttYrHg6yZ0S233MKpp57KfffdxxNPPMFRRx1Vb1nHjx/P3nvvzZlnnklVVRWbbLIJN910U93wm266ic0335wTTzyRddZZh2222YaHH364WfFAdgYFGn6+tb73ve9RXV3NxIkT+fKXv8yaa65JTU1NkxOIpjbnKb4gu1evXnTp0qVVeqh64403SCk1iK32/dtvv12vfLXVVqv3vmvXrg3Wz0q0aeIQEV2B/YAbylUtUdYgrYyIkRExJSKmzJ8/vyVClCRJKuuGG27gyCOPpKamhj322IMvf/nLjfaWVHyUv7ZZTPG+S/H71VdfnZ122oknnniiweMPf/gDlRoyZAiPPvoo7777bkXjvfvuu9x7772cc845fO9732O33XZj8ODBDS72XX311bn44ouZO3cu06ZNY8stt+Swww6rO/uw7rrrcs011/DWW2/xyCOPsNpqq7HvvvtWHE+te++9l+7duzNo0KCSwzt16sTJJ5/MCy+8wMyZMznhhBOoqanh6quvbtL0m3p2Zt68efXev/POO3zyySf0798foO5sROF9J1JK/Pe//23S9AutueaaRESDedY211rStRgtoa3POHwVeDKlVNsYbW5E9AfIn2s/hdnAOgXjrQ28XjyxlNL4lNLglNLg4ixdkiSptSxYsIBu3brVK5s4cWKTxt1yyy3p0qULt956a13Z4sWLG/R4M3ToUF544QU22GADBg8eXO8xcOBAIDuCDA3PcpQycuRIUkqccsopJYffeeedJctrey0qXN533nmHu+66q2T9iGDQoEGMHTuWTz/9tF5zJsh26HfccUdOP/103nvvvWY1nZk6dSrjx4/n2GOPbdATVSnrrbceZ5xxBuussw7PPfccUNlntyR33313vWncdNNNrLDCCnXXn9ReizF9+vS6Og8//HCD+TblbEC3bt3YcsstG/RoNWnSJDp37sx22223VMtSTlvfx+EbfNZMCeA24BhgbP58a0H5tRFxIdnF0VXA5DaMU5IkqVF77LEHV1xxBVtvvTXrrbceV111VZN3gPv378/w4cM59dRTiQiqqqoYP348CxcuZIUVPjum++1vf5vLLruMIUOG8MMf/pD111+fN998k0cffZT111+fUaNGscoqq9C/f3+uv/56qqqq6NatW6M3QVtvvfW44oor+OY3v8mrr75a13Xq7Nmzufbaa5k6dSqvv97gOC39+vVj880358wzz6R79+4sXryYc889lzXWWKPeUfTtttuOYcOGMXDgQFJKXHLJJayyyipss802zJ07l4MPPpijjjqKqqoqPvzwQy644ALWXnvtRnsPqvXmm2/y2GOPsXjx4robwF1++eVsscUWnH/++Y2Od+yxx7LWWmux7bbbssoqq3Dvvfcya9asuouya7tfvfjiizn44IPp2bNnXUJWic6dO7Pvvvvygx/8gFmzZnHyySczbNiwugujd955Z/r27cuoUaOorq5m3rx5jBs3jpVWWqnedDbZZBP+8pe/cN9999GrVy823HBDevXq1WB+Z511Fvvttx8jR47kkEMO4cknn+Tss89m1KhRrX6juDZLHCJiRWAP4LiC4rHApIgYAbwGHAqQUno2IiYBzwGLgFH2qCRJkjqKn/70p7zzzjuMHj2aFVZYgUMPPZSf//zn9XrtWZJf/vKXLF68mJ/85Cd07tyZ4cOHs/HGG/O73/2urs6KK67IQw89xBlnnMFPfvIT5s+fT79+/dh+++057LDD6upddtllnHLKKQwdOpSFCxcyZ86ckr0EQXa/h4022oixY8dy/PHH884779CvXz9233137r777kbjnTRpEscddxxHHnkkffr04aSTTmLu3Ln8/ve/r6uzww47cMUVVzBz5ky6dOnC1ltvzT333EO/fv344IMP2HjjjbnwwguZNWsWPXv2ZMcdd+TSSy+lS5cuS/ys7rnnHu655x46d+5Mr1692GKLLRg3bhzf+ta36s4alLLjjjty5ZVX8pvf/IaPP/6YqqoqJkyYwFe/+lUAvvjFL3LuuedyySWXMG7cOKqqqhrtOWlJansoGj58OAsWLPcbmdEAACAASURBVODAAw/kN7/5Td3w7t27c/PNN3P88cdz8MEHs+mmm3L55ZfXuzgeYMyYMRx33HEcfPDBvPfee1x33XUMGzaswfy+/vWvc80113DuuecyYcIE+vXrx2mnncYZZ5xRceyVinJXpH9eDB48ODW3hwFVbuy0N5d6GqO36t0CkUiStGzYeeedWWmllbjnHm9dpfYVEVNTSoOLy9u6qZIkSdJy79577+Wpp55iq622YuHChUycOJFHHnmE22+/vb1Dkxpl4iBJktTGevbsyY033shPf/pTFi5cyCabbMK1117Lvvvu296hSY0ycZCWoCWaZEHHb5a1vCynJHUUO+64I5Mn2++LPl/a4wZwkiRJkj5nTBwkSZIklWXiIEmSJKksEwdJkiRJZZk4SJIkSSrLxEGSJElSWSYOkiRJksoycZAkSZJUlomDJEmSpLJMHCRJkiSV1bm9A5AkSeWNnfbmUk9j9Fa9WyASScsrzzhIkiRJKsvEQZIkSVJZJg6SJEmSyjJxkCRJklSWiYMkSZKkskwcJEmSJJVl4iBJkiSpLBMHSZIkSWWZOEiSJEkqy8RBkiRJUlkmDpIkSZLKarPEISJWi4gbI+L5iJgeETtExOoRcV9EvJQ/9yqof2pEzIiIFyJir7aKU5IkSVJDbXnG4SLg7pTSJsCWwHRgNPBASqkKeCB/T0RsCgwDBgJ7AxdHRKc2jFWSJElSgTZJHCJiFeD/AVcApJQ+Tin9B9gfuCqvdhVwQP56f+D6lNLClNIrwAxg27aIVZIkSVJDbXXGYQNgPvC7iJgWEZdHxEpAv5TSHID8uW9efy1gVsH4s/MySZIkSe2grRKHzsDWwCUppa2AD8ibJTUiSpSlBpUiRkbElIiYMn/+/JaJVJIkSVIDbZU4zAZmp5Qez9/fSJZIzI2I/gD587yC+usUjL828HrxRFNK41NKg1NKg/v06dNqwUuSJEnLuzZJHFJKbwCzImLjvGgo8BxwG3BMXnYMcGv++jZgWER0i4j1gSpgclvEKkmSJKmhzm04rxOAiRHRFXgZOJYscZkUESOA14BDAVJKz0bEJLLkYhEwKqX0aRvGKkmSJKlAmyUOKaWngMElBg1tpP45wDmtGpQkSZKkJvHO0ZIkSZLKMnGQJEmSVJaJgyRJkqSyTBwkSZIklWXiIEmSJKksEwdJkiRJZZk4SJIkSSrLxEGSJElSWSYOkiRJksoycZAkSZJUlomDJEmSpLJMHCRJkiSVZeIgSZIkqSwTB0mSJElldW7vAJYlY6e92SLTGb1V7xaZjiRJktRSPOMgSZIkqSwTB0mSJEllmThIkiRJKsvEQZIkSVJZJg6SJEmSyjJxkCRJklSWiYMkSZKkskwcJEmSJJVl4iBJkiSpLBMHSZIkSWWZOEiSJEkqq80Sh4iYGRH/jIinImJKXrZ6RNwXES/lz70K6p8aETMi4oWI2Kut4pQkSZLUUFufcdgtpTQopTQ4fz8aeCClVAU8kL8nIjYFhgEDgb2BiyOiUxvHKkmSJCnX3k2V9geuyl9fBRxQUH59SmlhSukVYAawbTvEJ0mSJIm2TRwScG9ETI2IkXlZv5TSHID8uW9evhYwq2Dc2XlZPRExMiKmRMSU+fPnt2LokiRJ0vKtcxvOa6eU0usR0Re4LyKeX0LdKFGWGhSkNB4YDzB48OAGwyVJkiS1jDY745BSej1/ngfcTNb0aG5E9AfIn+fl1WcD6xSMvjbwelvFKkmSJKm+NkkcImKliFi59jWwJ/AMcBtwTF7tGODW/PVtwLCI6BYR6wNVwOS2iFWSJElSQ23VVKkfcHNE1M7z2pTS3RHxBDApIkYArwGHAqSUno2IScBzwCJgVErp0zaKVZL0OTF22pstMp3RW/VukelI0rKsyYlD1ES3VJ0WNmcmKaWXgS1LlL8FDG1knHOAc5ozP0mSJEktq5KmSnOiJi6Kmtis1aKRJEmS1CFVkjgMA9YEpkRNPBY18a2oiRVbKS5JkiRJHUiTE4dUne5N1elwsvsp/AH4AdlZiN9GTWzTWgFKkiRJan8V96qUqtNbqTr9AjiO7I7OI4GHoyb+HjXR4DoGSZIkSZ9/FfWqFDXRG/gmMALoDVxF1oRpDvC/wI1kXadKkiRJWoZU0qvSjcA+wKPA2cAfU3X6pGD4WcCPWjxCSZIkSe2ukjMOM4EtU3V6sdTAVJ1S1MQXWyQqSZIkSR1KJYnDWWQ3Y6uT96rUOVWndwFSdZrTgrFJkiRJ6iAquTj6DmCLorItgNtaLhxJkiRJHVElicNmwOSisidomExIkiRJWsZUkjgsBIpv+LYiRc2XJEmSJC17KkkcHgHOipqIgrJq4O8tG5IkSZKkjqaSi6N/DPwZOCRq4l/ABkACdm+NwCRJkiR1HE1OHFJ1eiVqYjNgf2A9su5Zb0vV6YNWik2SJElSB1HRnaPzJOHaVopFkiRJUgdVUeIQNXEoMBhYubA8Vaf/acmgJEmSJHUsTU4coiZ+AxwB/AWweZIkSZK0HKnkjMPhwLapOr3UWsFIkiRJ6pgq6Y71I7ILoiVJkiQtZypJHH4O/KS1ApEkSZLUcVXSVOk4YMOoiROAuYUDUnXatEWjkiRJktShVJI4/LzVopAkSZLUoVVyA7grWjMQSZIkSR1Xpfdx2AE4GuifqtOBURNbASul6vS3VolOkiRJUodQyX0chgGXANcBQ/PiFYCzgN1bPjRJkrS8GTvtzRaZzuiterfIdCR9ppJelU4H9srvEv1pXvZPYLOmTiAiOkXEtIi4I3+/ekTcFxEv5c+9CuqeGhEzIuKFiNirgjglSZIktbBKEoe1UnWanL9O+fMioFMF0/g+ML3g/WjggZRSFfBA/p6I2BQYBgwE9gYujohK5iNJkiSpBVWSOLwcNbF9Udn2QJPuJB0RawP7AJcXFO8PXJW/vgo4oKD8+pTSwpTSK8AMYNsKYpUkSZLUgipJHM4Fbo2aGA10iZr4Ptn1Dj9t4vi/BE4GFheU9UspzQHIn/vm5WsBswrqzc7LJEmSJLWDJicOqTr9ERgO7Ar8G/ga8N1Une4oN25E7AvMSylNbeLsolQIJaY7MiKmRMSU+fPnN3HSkiRJkipVUXesqTr9CfhTM+azE7BfRHwN6A6sEhG/B+ZGRP+U0pyI6A/My+vPBtYpGH9t4PUG8aQ0HhgPMHjw4AaJhSRJkqSWUUl3rI1eY1Bw0XTp4SmdCpwKEBFDgB+llI6KiAuAY4Cx+fOt+Si3AddGxIXAF4AqYInzkCRJktR6Kjnj8FiJstqj/M3t8WgsMCkiRgCvAYcCpJSejYhJwHNkPTeNSil92vhkJEmSJLWmShKHLkXv1wLOJjs70GQppQeBB/PXb/HZzeSK650DnFPJtCVJkiS1jiYnDqm6wRH/16ImTgCmAn9s0agkSZIkdSiVdMdaShc+60JVkiRJ0jKqkoujTy4qWgk4kOyOz5IkSZKWYZVc4/D1ovfvk/WCNK7lwpEkSZLUEVVyjcMurRmIJEmSpI5raa9xkCRJkrQcqOQah0/47L4NjUrVqetSRSRJkiSpw6nkGocfA98DfgG8AmwAnAj8Fniq5UOTJEmS1FFUkjgcDeyTqtOM2oKoifuB61N1uqjFI5MkSZLUYVRyjUMV8FpR2WvARi0XjiRJkqSOqJLEYRowNmqiG0DURFfgXOAfrRGYJEmSpI6jkqZK3wHuAEZGTcwF+gFzaHh/B0mSJEnLmEru4/Bi1MSmwE7AWsC/gUdSdVrUWsFJkiRJ6hgqOeNAniQ8FDXRJ1Wn+a0UkyRJkqQOppL7OKxI1hXr0WT3c1gpamJ/YLNUnc5ppfgkSZIkdQCVXBz9c2AdYDfgk7xsKnBESwclSZIkqWOpJHHYDzgiVafHgcUAqTrNJrveQZIkSdIyrJLEoRPwYWFB1MRKwPstGpEkSZKkDqeSxOHvwMlFZaOAh1ouHEmSJEkdUSW9Kv0v8OeoiaOAnlET04CeZNc8SJIkSVqGVXIfh5lREwPJrnVYH3gVuC1Vpw9aKzhJkiRJHUOTEoeoic7AH4HDU3X6Q+uGJEmSJKmjadI1DvmN37YHvEu0JEmStByq5OLoicB3WysQSZIkSR1XJRdHDwSOj5o4HphJfi8HgFSdvtbCcUmSJEnqQCpJHCbnD0mSJEnLmbKJQ9TE+FSdRqbqdEb+fttUnSpKICKiO/Aw0C2f540ppeqIWB34AzCA7CzGYSmld/JxTgVGAJ8CJ6aU7qlknpIkSZJaTlOucRhW9P7uZsxnIbB7SmlLYBCwd0RsD4wGHkgpVQEP5O+JiE3z+Q4E9gYujohOzZivJEmSpBbQlMQhyrwvK2Xez992yR8J2B+4Ki+/Cjggf70/cH1KaWFK6RVgBrBtpfOVJEmS1DKakjikMu+bJCI6RcRTwDzgvpTS40C/lNIcgPy5b159LWBWweiz87LiaY6MiCkRMWX+/PnNCUuSJElSEzTl4uiuUROnFbzvXvSeVJ3OLTeRlNKnwKCIWA24OSI2W0L1Umc1GiQsKaXxwHiAwYMHNyuhkSRJklReUxKHx4A9Ct4/XvQ+AWUTh7rKKf0nIh4ku3ZhbkT0TynNiYj+ZGcjIDvDsE7BaGsDrzd1HpIkSZJaVtnEIVWnIUs7k4joA3ySJw09gK8A5wO3AccAY/PnW/NRbgOujYgLgS8AVdgVrCRJktRuKrmPw9LoD1yV94y0AjAppXRHRDwKTIqIEcBrwKEAKaVnI2IS8BywCBiVN3WSJEmS1A7aJHFIKT0NbFWi/C1gaCPjnAOc08qhSZIkSWqCpvSqJEmSJGk5Z+IgSZIkqSwTB0mSJEllmThIkiRJKsvEQZIkSVJZJg6SJEmSyjJxkCRJklSWiYMkSZKkskwcJEmSJJVl4iBJkiSpLBMHSZIkSWV1bu8AJEmStOwaO+3NpZ7G6K16t0AkWlqecZAkSZJUlomDJEmSpLJMHCRJkiSVZeIgSZIkqSwTB0mSJEllmThIkiRJKsvEQZIkSVJZJg6SJEmSyjJxkCRJklSWiYMkSZKkskwcJEmSJJVl4iBJkiSpLBMHSZIkSWW1SeIQEetExF8iYnpEPBsR38/LV4+I+yLipfy5V8E4p0bEjIh4ISL2aos4JUmSJJXWVmccFgH/m1L6ErA9MCoiNgVGAw+klKqAB/L35MOGAQOBvYGLI6JTG8UqSZIkqUibJA4ppTkppSfz1+8B04G1gP2Bq/JqVwEH5K/3B65PKS1MKb0CzAC2bYtYJUmSJDXU5tc4RMQAYCvgcaBfSmkOZMkF0DevthYwq2C02XmZJEmSpHbQpolDRPQE/giclFJ6d0lVS5SlEtMbGRFTImLK/PnzWypMSZIkSUXaLHGIiC5kScPElNJNefHciOifD+8PzMvLZwPrFIy+NvB68TRTSuNTSoNTSoP79OnTesFLkiRJy7m26lUpgCuA6SmlCwsG3QYck78+Bri1oHxYRHSLiPWBKmByW8QqSZIkqaHObTSfnYCjgX9GxFN52WnAWGBSRIwAXgMOBUgpPRsRk4DnyHpkGpVS+rSNYpUkSZJUpE0Sh5TS3yh93QLA0EbGOQc4p9WCkiRJktRkbXXGQZI6hLHT3lzqaYzeqncLRCJJ0udLm3fHKkmSJOnzx8RBkiRJUlkmDpIkSZLKMnGQJEmSVJaJgyRJkqSy7FVJkiRJWkot0WsfdOye+zzjIEmSJKkszzhIkiS1A+8ro88bzzhIkiRJKsvEQZIkSVJZJg6SJEmSyjJxkCRJklSWiYMkSZKkskwcJEmSJJVl4iBJkiSpLO/jIEnLoOXhDqaSpLblGQdJkiRJZZk4SJIkSSrLxEGSJElSWSYOkiRJksoycZAkSZJUlomDJEmSpLJMHCRJkiSVZeIgSZIkqSwTB0mSJElltUniEBFXRsS8iHimoGz1iLgvIl7Kn3sVDDs1ImZExAsRsVdbxChJkiSpcW11xmECsHdR2WjggZRSFfBA/p6I2BQYBgzMx7k4Ijq1UZySJEmSSmiTxCGl9DDwdlHx/sBV+eurgAMKyq9PKS1MKb0CzAC2bYs4JUmSJJXWntc49EspzQHIn/vm5WsBswrqzc7LGoiIkRExJSKmzJ8/v1WDlSRJkpZnHfHi6ChRlkpVTCmNTykNTikN7tOnTyuHJUmSJC2/2jNxmBsR/QHy53l5+WxgnYJ6awOvt3FskiRJkgq0Z+JwG3BM/voY4NaC8mER0S0i1geqgMntEJ8kSZKkXOe2mElEXAcMAXpHxGygGhgLTIqIEcBrwKEAKaVnI2IS8BywCBiVUvq0LeKUJEmSVFqbJA4ppW80MmhoI/XPAc5pvYgkSZIkVaIjXhwtSZIkqYMxcZAkSZJUlomDJEmSpLJMHCRJkiSVZeIgSZIkqSwTB0mSJEllmThIkiRJKsvEQZIkSVJZJg6SJEmSyjJxkCRJklSWiYMkSZKkskwcJEmSJJVl4iBJkiSpLBMHSZIkSWWZOEiSJEkqy8RBkiRJUlkmDpIkSZLKMnGQJEmSVJaJgyRJkqSyTBwkSZIklWXiIEmSJKksEwdJkiRJZZk4SJIkSSrLxEGSJElSWSYOkiRJksrq0IlDROwdES9ExIyIGN3e8UiSJEnLqw6bOEREJ+A3wFeBTYFvRMSm7RuVJEmStHzqsIkDsC0wI6X0ckrpY+B6YP92jkmSJElaLnXkxGEtYFbB+9l5mSRJkqQ2Fiml9o6hpIg4FNgrpfTt/P3RwLYppRMK6owERuZvNwZeaPNAK9cbeLO9g2gDy8tywvKzrC7nsmd5WdblZTlh+VlWl3PZs7ws6+dlOddLKfUpLuzcHpE00WxgnYL3awOvF1ZIKY0HxrdlUEsrIqaklAa3dxytbXlZTlh+ltXlXPYsL8u6vCwnLD/L6nIue5aXZf28L2dHbqr0BFAVEetHRFdgGHBbO8ckSZIkLZc67BmHlNKiiDgeuAfoBFyZUnq2ncOSJEmSlksdNnEASCndBdzV3nG0sM9V06qlsLwsJyw/y+pyLnuWl2VdXpYTlp9ldTmXPcvLsn6ul7PDXhwtSZIkqePoyNc4SJIkSeogTBzUQEQcUHiX7og4KyK+soT6gyPiV82c12oR8T8F778QETc2Z1pLKyLGRMSPyi1vC86v3ufcniLixIiYHhET2zuWthQRf2/vGFpDRLzf3jG0hYg4KSJWLHh/V0SsVsH4+0XE6NaJrm00Z5vZVtu4lhARAyLimfaOoyOqdH1va8X/70s5rSERsWNLTKsjyNfrI5o5brtu322q9DkQEUH2XS1uo/lNAO5IKbX6DnxEDMjntVlrz6uciBgDvJ9S+nkbzW8CbfQ5NyGW54GvppReWYppdEopfdqCYamZIuL9lFLP9o6jtUXETGBwSunz0Ce6mqEj/Ue0tojonFJa1IR6bbpP0FyNfXfN+a9o6//n1hYRQ4AfpZT2LTFsietBu2/fU0o+mvkAbgGmAs8CI/Oy94FzgH8AjwH98vIN8/dPAGeR/QBqp/PjvPxpoCYvGwBMBy4GppHdiKPVYwV2BN4GXgGeyuOeABySj/Nl4O/5OJOBlYEhZBsHgDHANcCfgZeA7+TlPYEHgCeBfwL75+XXAwvyeV2QL/cz+bDuwO/y+tOA3fLy4cBNwN35PH62FJ/LT8huHHg/cB3wo6LlHQs8l383P1/Sd1n4OeTv/w8YXmo6pT7ndlyPfwt8nH/OPwGuzJdtWsH3NAD4a/79PQnsWLDMfwGuBZ5r799kM5b9fSDyde+Z/DM4PB92Te3y5+8nAvu1d8xNXa78ubFl+wPwtYL6E4CDyXqwu4DPtkfHtUPsP8zjfQY4KV/3ngeuymO6EVgROLFgvf1LPu5Mspsr1Y5zeT6dicBXgEfybca2ef3hwP/lrw/N6/4DeLhg+C3A7flv9fg8vmn5NmD1CparseXYBniIbPt8D9A/r39iwTbj+rxs13x78VQew8rU32Y2KV7Kb+MmAJeQ/bZfzud7Jdl/0oRmfq8rAXfmn+8zwOHAmfm69gzZBaO1BzO3yes9mq+PhctXctsP7JnXfxK4Aei5hOVr8F238DpcallnAr3z4YOBB/PXY/Jlv5dsOzocuDVfxheA6oL1p94+AZ+t7w3mV/A5Nli32vC3XPj//gQF/xWF621e90fAmFLrfl73DeDf+bR2aevtUtHveDpwGdn+1L1AD7L9grvzz/qvwCbFv7X8fe22+THgv/ny/CD/3m8g++3+mUb2mQqn0W6fQXvO/PP+4LONcI/8x7oGkICv5+U/A07PX98BfCN//d2ClWfPfKMRZE3H7gD+X75yLga2b4dYi1f0CcAhQFeyP5Ev5+WrkPXMNYT6icM/8vn0BmYBX8jrrZLX6Q3MyJe5eONR9x74X+B3+etNgNfIkonheRyr5u9fBdZpxmeyTf6DXDFflhkUJA7A6mQb7to/s9XKfJd1n0P+/v/yWBubTr3PuZ3X5Zn593IucFRtnMCLZH9KKwLd8/IqYErBMn8ArN/ey9DM5X6fbIf5PrKd5n75etafbGfplrzeqmQ7Yp3bO+amLlf+3NiyHQhcldfpmv9OewAj+Ww70A2Y0pbfbcFvciWyP85nga3ItlU75XWuJDtSV7felliPBwCLgM3JtqtT8/EC2L/gex3OZ4nDP4G1atf9guEzyHbQ+5D90X83H/YL4KQKlm1AieX4MdmBmD552eFkXY9DdsPTbkXx3F4wfk+y7eoA6u9Yl42X8tu4CWQ7bLWf17tFn+WgZny3BwOXFbxflYLEiyxRr/0/ehrYNX9dnDg02Pbn3/nDwEp5vVPIkpLGlq/Bd93C63GpZZ1J44nDVKBHwTLOIfuPrv2/HkyJfQI+W99Lza9LY+tWG/6eC9fNIRT8V7DkxKHUuj+G/Hffng8+27YMyt9PAo4i28mvysu2A/5c+FsrGL+x/YXhZDc+rt1XK7nPVDiN9np4jcPSOTEiao/Wr0O2Q/Ux2Y4lZBuDAfnrHciyScgy7lp75o9pZJnlJvl0AF5NKT3WDrE2ZmNgTkrpCYCU0rup9Om0W1NKC1LWfOAvwLZkf0DnRsTTZEf31yLbkVmSncn+TEgpPU/2J/HFfNgDKaX/ppQ+IjsysV6ZaZWyC3BzSunDlNK7NLzB4LvAR8DlEXEQ8GFe3th32ZjGptMR7QmMjoingAfJ/pzXJfsTuiwi/km27IXXZkxOS9HEqQPYGbgupfRpSmku2RG6L6eUHgI2ioi+wDeAPzayvndkJZcN+BOwe0R0A75KdtR1Adn3/838+3+cbOelqvSkWy3em1NKH6SU3ic7urwLMCul9Ehe5/d5vXJeSSn9M2XNOZ4l22Yksp3GASXqPwJMiIjvkCVatf6SUnovpTSfbEf89ry8seksSfFy7AVsBtyXf+anA2vnw58GJkbEUWQ7KrUxXhgRJ5LtUJVaHyuJd0nbptsLPq+5RZ9lpctdO/+vRMT5EbFLSum/wG4R8Xi+XdkdGBgRq+bL9lA+3jVF0ym17d+ebJv0SP45HpOXN7Z8jX3XLaXUsi7Jbfnvr9Z9KaW38rKb+Gx9b2yfoNT8Nqbxdau9NPW/otS635G8klJ6Kn9du++0I3BD/llfSnaAplL3pZTezl83Z5+pTXTo+zh0ZHn7tK8AO6SUPoyIB8l2sj7JN7YAn1L+Mw7gvJTSpUXTH0CWnXe0WFOZOpSok4AjyY6AbZNS+iRvm9y9CfNrzMKC102JvTGNLk/KbkK4LTCU7M7lx5P9uTVmEfU7HOjezOm0pwAOTim9UK8wa186F9iSbBk/KhjcIutpO1rSenYN2bo7DPhW24TTokouW0rpo3w7sBfZkcjrCuqfkFK6p23Ca6Cx76LUNqWcwm3E4oL3iymxvUgpfTcitgP2AZ6KiEHNmU4ZxXG/BzybUtqhRN19yM4+7wecEREDU0pjI+JO4GvAY/kFzh8VjdfkeMtsmxaWmEbJ6TRFSunFiNgmj/28iLgXGEV2jcqsfBvTnfL/M6W2/UG20/WN4sqllq/Ud51SeqvSZWpMI8ta+P9Q/N9XvA1tbH0vua1tZH430/i61V4K4y/5f5lrsO63QWyVKF4H+wH/SSn9//buN0auqozj+PenoLS2qRFKIir4B7RgjMofIyExISqvSgoRg6YihRAiUQyKIVGqN+PiC1L/EIpQtepGGy1EXphqak0IhICWhfQFfSFVCYVGKaXSYmkrtPTxxXPGHceZudvZ2Znu+vskm+zeO3vuOXPvPfee85xz7wc6fPY/5SxzU17XI93W76efe6ahcMShf4uAPeVGfAnZ49HLZjKcCFmBNW0Crpa0AEDSW0oP5yjzuo8Mdbd7AjhF0nkAkhZK6nQBWSbpBEknkuG4R0sedpUT4EImIwTdtgUZel5etvVusud7W5fP9uNB4FJJ8yQtBC5uXVn2yaLIFxHeADQrhW778mngLEmvL71mH61Jp1fZR2UTcH2p4JD0wbJ8ERltOgJcwcz00o3Kg8Dlkl4raTF5wZoo68bJfUbMzjfX9yrbeuAqske/2VDYBFwn6XjI807SG4ac30skzS/bvZQcL3yqpOYN0KeBh8rvAzuHJL0rIh6JiG8Au8nI7KC1l2MzsLi5TNLxkt4r6TXk8Mv7gZvIYYMLSh63RsSt5DCyJdPJTI+6aeAknQIciIh15Dyvs8uq3SUflwFExF7gRUnNXvblU0h+M3CBpNPLtuaXY7dj+WZ6X3cp63ZyKB5MXj+6+bikN0maB1xCRkiOdnvb6HBs9VmkfvU6P58DTpZ0Yol8LgXoduzXpDVq/wSekvRJyAaCpPeXdduZ3O/LyOg91Jen2z3TyDni0L/fAZ8rYaRtZMXVyw3AOkk3kpOYXgSIiN9LOhP4Y7lXe4kcLzfIp9McbV7Xk8NSvkipzEteX5F0ObC6VGgHyUhGuwmyjKcCYxHxd+VjiWCMoQAABHJJREFUPjdIeoycDPRESfMfkh5WPm5vI/D9lnTuBNaUMPZhcqLxy+V7mraI2CLp7pKfp8kblFYLgV9LavaCfaks77Yvd0i6hwyz/oUcftYrnf/6niPiyYEUbHrGgNuAx0vjYTtZod8J3FsqxvuZ/VGGpiB75s4n5+YEcFNE7ASIiOck/YmccDobdS0bOanvZ+QwiVfKsrVk2H1L2f/PkzcuQ1HOyXEmGzdrgT3kZMQrJf2APLfuKut/CGyU9GxEXDjNza+SdAZ5jt5HfmeDvpFuL8dqsrF2e+lsOI48//5M1jGLSn6+FxF7JY2Vm4hXyWE6G+lvSERTt7ppJryP/I6PAIeA68hjaytZzzza8tmrgJ9IOsBko7ariHhe0grgl+UmFHJozj46l6/Tvh6kTmWdB/xY0tfIYYC9PERGO08HfhERjylHIUx5e+V6fRn/e2wNrQOk7fp+kGwsNNcdkvRN8rt4inJPQHZKdTr2NwC/krSMjIq2X69HbTlwl6SVZONgPXlc/Yg8BifIY6157XwcOKwcQj5O1nOtOt4zHQv8ONYhUT5r/GBEhKRPkZNrl406X4OmOfbItE7+X/blXFciYlsiomtPTtnXW4GzpzBO2WaA5sjjOOdKOWxmlQbQuRHxhVHnxawTRxyG5xzgjtKLt5fZOV7akvflLFdC+w+QYf1un/kY+eSb77rRYGZm5oiDmZmZmZlNgSdHm5mZmZlZLTcczMzMzMyslhsOZmZmZmZWyw0HMzM7JqihNWrojlHnw8zMOvPkaDMzQw09QL7z4VDbqvOjiq0zsL3twMqoYt2g0zYzs5nhx7GamVnTWFRxy6gzYWZmxyY3HMzMrKcSjdgCvIN8W/wu4Fryza63kW+Jvw/4bFSxr/zPacDtwAXkW2PvBb4aVRxUQxvK/6xVQ2uAP0QVF6mhceBwVHFNXRplfQCfJ980vIR8K+6KqOKYecuqmdlc4jkOZmY2FVcAtwJvBO4Gfk42Hj4CvB14D3A9gBo6DvgtsBM4DfgwefP/bYCo4mLgGeCaqGJBVHFR+8bq0mixAvgEcBKwA1g9mOKamVk7NxzMzKzpZjW0t/WnZd09UcXmqOJVYB3wZmBVVPFCVPEC8BvgvPLZDwFnAF+OKvZHFX8DVgJXqyFNMS9TTWNVVPFMVPEyMA6c21/RzcysjocqmZlZ07d6zHF4tuX3A12WLSy/vw3YFVXsb1n/JHACsJgc6lRnqmm05mF/Sx7MzGzAHHEwM7NB2wGcrIbmtyx7J/AvYHf5+8gA0jAzsyFyxMHMzAZtAvgr8B01dCM5L2IM+GlU0Www7CSHIk0nDTMzGyJHHMzMrOnrauiltp+lR5tIVHEYWAq8lZwEPQE8Anyl5WO3AJ9RQ3vU0MY+0zAzsyHyC+DMzMzMzKyWIw5mZmZmZlbLDQczMzMzM6vlhoOZmZmZmdVyw8HMzMzMzGq54WBmZmZmZrXccDAzMzMzs1puOJiZmZmZWS03HMzMzMzMrJYbDmZmZmZmVuvfkNjc6aBV5hkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot class distribution\n",
    "plt.rcParams['figure.figsize'] = [13, 5]\n",
    "plt.bar(freqs.keys(), freqs.values(), width=0.3,color='skyblue')\n",
    "plt.text(10,700,\"Target Class Distribution\", fontsize=15, ha='center', va='center')\n",
    "t = plt.title(\"Emotions from COVID-19 Related Text\", fontsize=18)\n",
    "# t.set_color(\"m\")\n",
    "x = plt.xlabel(\"Emotion\", fontsize=13)\n",
    "x.set_color('g')\n",
    "y = plt.ylabel(\"Frequency\", fontsize=13)\n",
    "y.set_color('g')\n",
    "plt.savefig('class_distribution.png')\n",
    "# [i.set_color(\"c\") for i in plt.gca().get_xticklabels()]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# there is a clear class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
